Multi-omics integration reveals organ-specific biosynthesis of flavonoids and terpenoids in the medicinal plant Bidens alba
Bibliographic record
Abstract
Introduction Bidens alba is a traditional medicinal plant valued for its antioxidant, anti-inflammatory, and antimicrobial properties, largely attributed to flavonoids and terpenoids. However, the tissue-specific distribution and regulatory mechanisms of these metabolites remain poorly understood. Methods We employed a combined approach of reference-guided transcriptomics and widely targeted metabolomics to investigate the biosynthesis and accumulation of flavonoids and terpenoids in four tissues (flowers, leaves, stems, and roots) of B. alba . Results A total of 774 flavonoids and 311 terpenoids were identified. Flavonoids were enriched in aerial tissues, while certain sesquiterpenes and triterpenes accumulated in roots. Transcriptome profiling revealed tissue-specific expression of key biosynthetic genes, including CHS, F3H, FLS, HMGR, FPPS , and GGPPS , which corresponded with metabolite patterns. Several transcription factors, such as BpMYB1, BpMYB2 , and BpbHLH1 , were identified as candidates regulating flavonoid biosynthesis, with BpMYB2 and BpbHLH1 showing contrasting expression between flowers and leaves. For terpenoid biosynthesis, BpTPS1, BpTPS2 , and BpTPS3 were identified as putative regulators of enzymes including GPPS and DXR , with BpTPS2/3 highly expressed in flowers. Discussion These findings provide new insights into the transcriptional and metabolic basis of tissue-specific secondary metabolism in B. alba . The identified candidate genes and regulatory factors represent valuable targets for future functional validation and hold potential for medicinal development and molecular breeding of this species.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".